Data Scientist

Key Responsibilities:

• Build agentic workflows using LangChain/LangGraph and similar frameworks.
• Develop autonomous agents for data validation, reporting, document processing, and domain workflows.
• Deploy scalable, resilient agent pipelines with monitoring and evaluation
• Develop GenAI applications using models like GPT, Gemini, and LLaMA.
• Implement RAG, vector search, prompt orchestration, and model evaluation.
• Partner with data scientists to productionize POCs.
• Build distributed data pipelines (Python, PySpark).
• Develop APIs, SDKs, and integration layers for AI-powered applications.
• Optimize systems for performance and scalability across cloud/hybrid environments.
• Contribute to CI/CD workflows for AI modelsdeployment, testing, monitoring.
• Implement of governance, guardrails, and reusable GenAI frameworks.
• Work with analytics, product, and engineering teams to define and deliver AI solutions.
• Participate in architecture reviews and iterative development cycles.
• Support knowledge sharing and internal GenAI capability building.

Key Skills & Requirements:

• 5-8 years of experience in AI/ML engineering, data science, or software engineering, with at least 4 years focused on GenAI.
• Strong programming expertise in Python, distributed computing using PySpark, and API development.
• Hands on experience with LLM frameworks (LangChain, LangGraph, Transformers, OpenAI/Vertex/Bedrock SDKs).
• Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration.
• Solid understanding of GenAI concepts: prompting, embeddings, RAG, evaluation metrics, hallucination identification, model selection, fine tuning, context engineering.
• Experience with cloud platforms (Azure/AWS/GCP), containerization (Docker), and CI/CD pipelines for ML/AI.
• Strong problem solving, system design thinking, and ability to translate business needs into scalable AI solutions.
• Excellent verbal, written communication, and presentation skills.

Good to Have:

• Experience in workflow automation and building reusable AI components.
• Background in analytics, statistical models, or enterprise data products.
• Experience with MLOps / LLMOps tooling